Real-time, distributed decision-making and data-processing has become necessary as high-throughput proteomics across geographic boundaries becomes more mature. The most common instrument used to characterize and analyze proteins is the Mass Spectrometer. As more Mass Spectrometers are used in parallel to create a high-throughput proteomics system, the data processing needs grow exponentially. Using a cluster of low-cost instrument routers, developed at Userspace Corporation, the goal for this project is to divide the data processing tasks into a decision tree, which converts sequential tasking into parallel tasking or multi-threaded algorithms. These instruments can be controlled and the algorithms can independently process data in parallel or offline as they emerge in large data sets from one or many Mass Spectrometers. The First Phase of study will use an LC-MS (Liquid Chromatography - Mass Spectroscopy) system that uses the ICAT (Isotope Coded Affinity Tags) technology developed at the lab of Dr. Ruedi Aebersold at the University of Washington (who is also the co-founder of the Institute for Systems Biology: ISB). The data from the Mass Spectrometers is analyzed using the COMET algorithm, also developed at the ISB. Userspace Corporation and ISB are collaborating on using Userspace's routers and framework in its Proteomics lab. The first phase of this project will evaluate technologies and configuration required to process Mass Spectrometry data at the instrument level using a distributed network of the Userspace wireless instrument routers. The data will be processed in real-time as it becomes available to the router cluster and a rule-based decision matrix. The duration of this phase will be six months. The next phases would involve improving the data formatting, so that publication and data mining become science-centric and in a standardized XML (eXtensible Markup Language) representation. Other instruments, algorithms and processes will be added to the router library.